@inproceedings{1f4ac0a8c7334589bbb40772fb98ce8d,
title = "Modelling of Destinations for Data-driven Pedestrian Trajectory Prediction in Public Buildings",
abstract = "Public buildings such as shopping arcades and railway stations are environments in which pedestrian movement is of significance to many smart building applications. The data-driven approach of pedestrian trajectory prediction is effective in learning a reliable model that can represent complex human movement. Pedestrian trajectories are highly linked to the locations of facilities and services inside a building as pedestrians move towards these destinations for engagement. This paper suggests that the notion of destination is a strong predictor of pedestrian trajectories and proposes a novel enhancement of the data-driven approach for pedestrian tracking in public buildings. The method of destination-driven pedestrian trajectory prediction (DDPTP) first evaluates the most likely destinations of the pedestrian using the destination classifier (DC) and then predicts the future trajectories with the destination-specific trajectory model (DTM). The proposed solution has been evaluated on the NYGC and the ATC datasets and found to outperform state-of-the-art models. The notion of destination can be further developed into a region of interest of which the within-region and out-of-region features can be factored out for more effective learning.",
keywords = "deep learning, destination prediction, gated recurrent unit (GRU), pedestrian trajectory prediction, public buildings",
author = "Lui, {Andrew Kwok Fai} and Chan, {Yin Hei} and Leung, {Man Fai}",
note = "Funding Information: ACKNOWLEDGMENT The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E12/20). Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2021",
doi = "10.1109/BigData52589.2021.9671813",
language = "English",
series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
pages = "1709--1717",
editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, {Xiaohua Tony} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
}